/*
 * Copyright (c) 2017 Villu Ruusmann
 *
 * This file is part of JPMML-LightGBM
 *
 * JPMML-LightGBM is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * JPMML-LightGBM is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with JPMML-LightGBM.  If not, see <http://www.gnu.org/licenses/>.
 */
package org.jpmml.lightgbm;

import java.util.ArrayList;
import java.util.List;

import org.dmg.pmml.DataType;
import org.dmg.pmml.OpType;
import org.dmg.pmml.mining.MiningModel;
import org.dmg.pmml.regression.RegressionModel;
import org.jpmml.converter.CategoricalLabel;
import org.jpmml.converter.FieldNameUtil;
import org.jpmml.converter.FortranMatrixUtil;
import org.jpmml.converter.ModelUtil;
import org.jpmml.converter.Schema;
import org.jpmml.converter.mining.MiningModelUtil;

public class MultinomialLogisticRegression extends Classification {

	public MultinomialLogisticRegression(Section config){
		super(config);

		int num_class = config.getInt(Classification.CONFIG_NUM_CLASS);
		if(num_class < 3){
			throw new IllegalArgumentException("Multi-class classification requires three or more target categories");
		}
	}

	@Override
	public MiningModel encodeModel(List<Tree> trees, Integer numIteration, Schema schema){
		Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);

		List<MiningModel> miningModels = new ArrayList<>();

		CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

		for(int i = 0, rows = categoricalLabel.size(), columns = (trees.size() / rows); i < rows; i++){
			MiningModel miningModel = createMiningModel(FortranMatrixUtil.getRow(trees, rows, columns, i), numIteration, segmentSchema)
				.setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("lgbmValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.DOUBLE));

			miningModels.add(miningModel);
		}

		return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
	}
}